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Design, Automation, and Test in Europe

Spinto: high-performance energy minimization in spin glasses

Héctor J. García, Igor L. Markov

Published Mar 8, 2010• 1 citation
Publisher

Abstract

Abstract—With the prospect of atomic-scale computing, we study cumulative energy profiles of spin-spin interactions in nonferromagnetic lattices (Ising spin-glasses)—an established topic in solid-state physics that is now becoming relevant to atomicscale EDA. Recent proposals suggest non-traditional computing devices based on nature’s ability to find min-energy states. Spinto utilizes EDA-inspired high-performance algorithms to (i) simulate natural energy minimization in spin systems and (ii) study its potential for solving hard computational problems. Unlike previous work, our algorithms are not limited to planar Ising topologies. In one CPU-day, our branch-and-bound algorithm finds min-energy (ground) states on 100 spins, while our local search approximates ground states on 1,000, 000 spins. We use this computational tool to study the significance of hyper-couplings in the context of recently implemented adiabatic quantum computers. I.

DOI
10.5555/1870926.1870966

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Spinto: High-performance energy minimization in spin glasses

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With the prospect of atomic-scale computing, we study cumulative energy profiles of spin-spin interactions in non-ferromagnetic lattices (Ising spin-glasses)-an established topic in solid-state physics that is now becoming relevant to atomic-scale EDA. Recent proposals suggest non-traditional computing devices based on nature's ability to find min-energy states. Spinto utilizes EDA-inspired high-performance algorithms to (i) simulate natural energy minimization in spin systems and (ii) study its potential for solving hard computational problems. Unlike previous work, our algorithms are not limited to planar Ising topologies. In one CPU-day, our branch-and-bound algorithm finds min-energy (ground) states on 100 spins, while our local search approximates ground states on 1,000,000 spins. We use this computational tool to study the significance of hyper-couplings in the context of recently implemented adiabatic quantum computers.

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Quantum computation using continuous-time evolution under a natural hardware Hamiltonian is a promising near- and mid-term direction toward powerful quantum computing hardware. We investigate the performance of continuous-time quantum walks as a tool for finding spin glass ground states, a problem that serves as a useful model for realistic optimization problems. By performing detailed numerics, we uncover significant ways in which solving spin glass problems differs from applying quantum walks to the search problem. Importantly, unlike for the search problem, parameters such as the hopping rate of the quantum walk do not need to be set precisely for the spin glass ground state problem. Heuristic values of the hopping rate determined from the energy scales in the problem Hamiltonian are sufficient for obtaining a better quantum advantage than for search. We uncover two general mechanisms that provide the quantum advantage: matching the driver Hamiltonian to the encoding in the problem Hamiltonian, and an energy redistribution principle that ensures a quantum walk will find a lower energy state in a short timescale. This makes it practical to use quantum walks for solving hard problems, and opens the door for a range of applications on suitable quantum hardware.

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Finding the ground state of an Ising-spin glass on general graphs belongs to the class of NP-hard problems, widely believed to have no efficient polynomial-time algorithms for solving them. An approach developed in computer science for dealing with such problems is to devise approximation algorithms that run in polynomial time, and provide solutions with provable guarantees on their quality in terms of the optimal unknown solution. Recently, several algorithms for the Ising-spin glass problem on a graph that provide different approximation guarantees were introduced albeit without implementation. Also recently, D-wave company constructed a physical realization of an adiabatic quantum computer, and enabled researchers to access it. D-wave is particularly suited for computing an approximation for the ground state of an Ising spin glass on its chimera graph -- a graph with bounded degree. In this work, we compare the performance of a recently developed approximation algorithm for solving the Ising spin glass problem on graphs of bounded degree against the D-wave computer. We also compared a heuristic tailored specifically to handle the fixed D-wave chimera graph. D-wave computer was able to find better approximations to all the random instances we studied. Furthermore the convergence times of D-wave were also significantly better. These results indicate the merit of D-wave computer under certain specific instances. More broadly, our method is relevant to other performance comparison studies. We suggest that it is important to compare the performance of quantum computers not only against exact classical algorithms with exponential run-time scaling, but also to approximation algorithms with polynomial run-time scaling and a provable guarantee on performance.

arXiv:2105.00537•May 2, 2021•2 citations
Abstract—With the prospect of atomic-scale computing, we study cumulative energy profiles of spin-spin interactions in nonferromagnetic lattices (Ising spin-glasses)—an established topic in solid-state physics that is now becoming relevant to atomicscale EDA. Recent proposals suggest non-traditional computing devices based on nature’s ability to find min-energy states. Spinto utilizes EDA-inspired high-performance algorithms to (i) simulate natural energy minimization in spin systems and (ii) study its potential for solving hard computational problems. Unlike previous work, our algorithms are not limited to planar Ising topologies. In one CPU-day, our branch-and-bound algorithm finds min-energy (ground) states on 100 spins, while our local search approximates ground states on 1,000, 000 spins. We use this computational tool to study the significance of hyper-couplings in the context of recently implemented adiabatic quantum computers. I.
With the prospect of atomic-scale computing, we study cumulative energy profiles of spin-spin interactions in non-ferromagnetic lattices (Ising spin-glasses)-an established topic in solid-state physics that is now becoming relevant to atomic-scale EDA. Recent proposals suggest non-traditional computing devices based on nature's ability to find min-energy states. Spinto utilizes EDA-inspired high-performance algorithms to (i) simulate natural energy minimization in spin systems and (ii) study its potential for solving hard computational problems. Unlike previous work, our algorithms are not limited to planar Ising topologies. In one CPU-day, our branch-and-bound algorithm finds min-energy (ground) states on 100 spins, while our local search approximates ground states on 1,000,000 spins. We use this computational tool to study the significance of hyper-couplings in the context of recently implemented adiabatic quantum computers.
Energy minimization of Ising spin-glasses has played a central role in statistical and solid-state physics, facilitating studies of phase transitions and magnetism. Recent proposals suggest using Ising spin-glasses for non-traditional computing as a way to harness the nature's ability to find min-energy configurations, and to take advantage of quantum tunneling to boost combinatorial optimization. Laboratory demonstrations have been unconvincing so far and lack a non-quantum baseline for definitive comparisons. In this work we (i) design and evaluate new computational techniques to simulate natural energy minimization in spin glasses and (ii) explore their application to study design alternatives in quantum adiabatic computers. Unlike previous work, our algorithms are not limited to planar Ising topologies. In one CPU-day, our branch-and-bound algorithm finds ground states on 100 spins, while our local search approximates ground states on 1, 000, 000 spins. We use this computational tool as a simulator to study the significance of hyper-couplings in the context of recently implemented adiabatic quantum computers.
Abstract Quantum computation using continuous-time evolution under a natural hardware Hamiltonian is a promising near- and mid-term direction toward powerful quantum computing hardware. We investigate the performance of continuous-time quantum walks as a tool for finding spin glass ground states, a problem that serves as a useful model for realistic optimization problems. By performing detailed numerics, we uncover significant ways in which solving spin glass problems differs from applying quantum walks to the search problem. Importantly, unlike for the search problem, parameters such as the hopping rate of the quantum walk do not need to be set precisely for the spin glass ground state problem. Heuristic values of the hopping rate determined from the energy scales in the problem Hamiltonian are sufficient for obtaining a better quantum advantage than for search. We uncover two general mechanisms that provide the quantum advantage: matching the driver Hamiltonian to the encoding in the problem Hamiltonian, and an energy redistribution principle that ensures a quantum walk will find a lower energy state in a short timescale. This makes it practical to use quantum walks for solving hard problems, and opens the door for a range of applications on suitable quantum hardware.
Quantum computation using continuous-time evolution under a natural hardware Hamiltonian is a promising near- and mid-term direction toward powerful quantum computing hardware. We investigate the performance of continuous-time quantum walks as a tool for finding spin glass ground states, a problem that serves as a useful model for realistic optimization problems. By performing detailed numerics, we uncover significant ways in which solving spin glass problems differs from applying quantum walks to the search problem. Importantly, unlike for the search problem, parameters such as the hopping rate of the quantum walk do not need to be set precisely for the spin glass ground state problem. Heuristic values of the hopping rate determined from the energy scales in the problem Hamiltonian are sufficient for obtaining a better quantum advantage than for search. We uncover two general mechanisms that provide the quantum advantage: matching the driver Hamiltonian to the encoding in the problem Hamiltonian, and an energy redistribution principle that ensures a quantum walk will find a lower energy state in a short timescale. This makes it practical to use quantum walks for solving hard problems, and opens the door for a range of applications on suitable quantum hardware.
Spin glasses are disordered magnets with random interactions that are, generally, in conflict with each other. Finding the ground states of spin glasses is not only essential for the understanding of the nature of disordered magnetic and other physical systems, but also useful to solve a broad array of hard combinatorial optimization problems across multiple disciplines. Despite decades-long efforts, an algorithm with both high accuracy and high efficiency is still lacking. Here we introduce DIRAC - a deep reinforcement learning framework, which can be trained purely on small-scale spin glass instances and then applied to arbitrarily large ones. DIRAC displays better scalability than other methods and can be leveraged to enhance any thermal annealing method. Extensive calculations on 2D, 3D and 4D Edwards-Anderson spin glass instances demonstrate the superior performance of DIRAC over existing methods. As many hard combinatorial optimization problems have Ising spin glass formulations, our results suggest a promising tool in solving these hard problems. Moreover, the presented algorithm will help us better understand the nature of the low-temperature spin-glass phase, which is a fundamental challenge in statistical physics.
Finding the ground state of an Ising-spin glass on general graphs belongs to the class of NP-hard problems, widely believed to have no efficient polynomial-time algorithms for solving them. An approach developed in computer science for dealing with such problems is to devise approximation algorithms that run in polynomial time, and provide solutions with provable guarantees on their quality in terms of the optimal unknown solution. Recently, several algorithms for the Ising-spin glass problem on a graph that provide different approximation guarantees were introduced albeit without implementation. Also recently, D-wave company constructed a physical realization of an adiabatic quantum computer, and enabled researchers to access it. D-wave is particularly suited for computing an approximation for the ground state of an Ising spin glass on its chimera graph -- a graph with bounded degree. In this work, we compare the performance of a recently developed approximation algorithm for solving the Ising spin glass problem on graphs of bounded degree against the D-wave computer. We also compared a heuristic tailored specifically to handle the fixed D-wave chimera graph. D-wave computer was able to find better approximations to all the random instances we studied. Furthermore the convergence times of D-wave were also significantly better. These results indicate the merit of D-wave computer under certain specific instances. More broadly, our method is relevant to other performance comparison studies. We suggest that it is important to compare the performance of quantum computers not only against exact classical algorithms with exponential run-time scaling, but also to approximation algorithms with polynomial run-time scaling and a provable guarantee on performance.